We find that existing methods perform poorly out-of-the-box on the proposed iPhone dataset with more diverse and complex real-life motions.
In Figure REF  (Bottom), we demonstrate this finding with HyperNeRF [1]} for it achieves the highest mLPIPS metric on the Nerfies-HyperNeRF dataset.
Shown in the 3rd column, HyperNeRF produces visually implausible results with ghosting effects. Thus we explored incorporating additional regularizations from recent advances in neural rendering.
Concretely, we consider the following: (+B) random background compositing [2]}; (+D) a depth loss on the ray matching distance [3]}, [4]}; and (+S) a sparsity regularization for scene surface [5]}.
In Figure REF  (Top), we show quantitative results from the ablation.
In Figure REF  (Bottom), we show visualizations of the impact of each regularization.
Adding additional regularizations consistently boosts model performance.
While we find the random background compositing regularizations particularly helpful, extra depth supervision and surface regularization further improve the quality, e.g., the fan region of the paper windmill.
